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Generating a Simulation-Based Contacts Matrix for Disease Transmission Modeling at Special Settings

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Generating a Simulation-Based Contacts Matrix for Disease Transmission Modeling at Special Settings Generating a Simulation-Based Contacts Matrix for Disease Transmission Modeling at Special Settings Mahdi M. Najafabadi a*, Ali Asgary a, Mohammadali Tofighi a, and Ghassem Tofighi b Author Affiliations: a Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM), York University, Toronto, Canada; b School of Applied Computing, Sheridan College, Toronto, Canada Abstract Since a significant amount of disease transmission occurs through human-to-human or social contact, understanding who interacts with whom in time and space is essential for disease transmission modeling, prediction, and assessment of prevention strategies in different environments and special settings. Thus, measuring contact mixing patterns, often in the form of a contacts matrix, has been a key component of heterogeneous disease transmission modeling research. Several data collection techniques estimate or calculate a contacts matrix at different geographical scales and population mixes based on surveys and sensors. This paper presents a methodology for generating a contacts matrix by using high fidelity simulations which mimic actual workflow and movements of individuals in time and space. Results of this study show that such simulations can be a feasible, flexible, and reasonable alternative method for estimating social contacts and generating contacts mixing matrices for various settings under different conditions. Keywords: Agent-Based Modeling, ABM, Contact Matrices, COVID-19, Disease Transmission Modeling, High Fidelity Simulations, Proximity Matrix, WAIFW Page 1 1. Introduction and Background Person-to-person disease transmission is largely driven by who interacts with whom in time and space (Prem et al., 2020). Past and current studies, especially those conducted during the COVID- 19 pandemic have further revealed that individuals who have more person-to-person contact per period have higher chances of becoming infected, infecting others, and being infected earlier during pandemics (Smieszek et al., 2014). Moreover, during pandemics, contact patterns can drastically shift from their baseline conditions due to individuals’ actions and public health measures (Prem et al., 2020). Therefore, estimating the baseline contact patterns, as well as how they would shift over time, have been important components of disease transmission modelling and predictions (Hens et al., 2009). These patterns are often generated in the form of contact matrices and are used in mathematical models to develop 'Who Acquires Infection from Whom' (WAIFW) matrices which determine the force of infections from infected individuals to susceptible populations (Beutels et al., 2006). The accuracy of the disease models and their effectiveness in public health decisions during disease outbreaks and pandemics very much depends on access to, and availability of, detailed and readily available data about contact mixing patterns (McCarthy et al., 2020). The more knowledge we have about the contact mixing patterns, the better we can understand and predict infectious disease dynamics and assess the effects of various public health interventions and measures that aim to control the spread of directly transmitted infections (Iozzi et al., 2010; Smieszek et al., 2014). Measuring contact mixing patterns has been an important, challenging, and expensive part of disease modeling research. During the past two decades, several methods have been proposed and used by researchers for calculating social contacts at different geographical scales and population mixes. These methods can be classified into survey- based, device-based, model-based, and simulation-based methods. Surveys are among the most widely used techniques in contacts measurement research and practice (Smieszek et al., 2014). Surveys collect contact data among individuals at different age groups or socio-economic settings (home, school, workplace, community). Using surveys, Page 2 participants are asked to record or recall all contacts that they have had with others in all spaces they have gone to or visited during a specified period of time, such as a day or week. Surveys collect data through direct observation, diaries, questionnaires, direct interviews, phone interviews, and web-based questionnaires. The POLYMOD study used a survey-based approach in eight European countries. Participants were asked to complete a contact diary to record details about all the people they met over the course of a single day (Eames et al., 2011; Grantz et al., 2020). The contacts, along with the demographic and geographic data collected from the participants, were then used to develop baseline contact patterns (Hens et al., 2009). Device-based methods, on the other hand, collect contact data through a variety of wearable devices and sensors, usually radio-frequency identification (RFID) tags or smartphones. Some of these devices keep a log of the physical location of individuals per time-step (i.e., the interval by which they record the physical location data). This data is then analyzed to find proximities among different individuals. Other types of devices only record or transmit the data regarding the proximity of individuals with relevant timestamps which allow the calculation of duration for each recorded effective contact. The use of wearable devices is a practical way of calculating contacts in small settings such as schools, workplaces, and hospitals (Champredon et al., 2018; Duval et al., 2018). Moreover, advancements in smartphone technology and wider access to them have made large-scale device-based contact measurement faster, more accurate, and more feasible. Collecting digital device-based data is a quick alternative in situations where survey data is hard to collect or unavailable. Some recent studies have demonstrated the successful collection of device-based contact data at different scales (Watson et al., 2017). However, this type of contact measurement faces several technical, financial, privacy, and accuracy challenges that limit its utilization. More specifically, there is a large inequity of access to these devices and technologies around the world. Model-based contact mixing studies often use data collected from surveys and sensors to customize and replicate social contact matrices for other communities and settings. They also try to mathematically model particular communities or settings to calculate potential contacts. Page 3 Many of the current COVID-19 related disease transmission models in different countries and regions use mathematical approaches to recreate contact matrices for desired communities and settings based on the POLYMOD study (McCarthy et al., 2020). Finally, the simulation-based method aims to recreate detailed synthetic or use high-fidelity simulations of desired settings to estimate potential human contacts in them. An example of this approach is the Little Italy simulation that created a synthetic society to reconstruct contact data using an individual-based model (IBM) and Time Use Survey data (Iozzi et al., 2010). A comparison of contact matrices generated by this simulation with those developed for Italy by the POLYMOD study showed that simulation-based matrices can provide a fruitful complementary approach to survey-based matrices. With advances in simulation software and hardware, especially agent- based and discrete event simulations, and the availability of supporting data through other methods and tools, this approach is promising. Especially in situations where the accessing of special settings is not quite feasible due to privacy concerns or ongoing outbreaks. Despite their high potential, the use of simulation-based methods for this purpose has been very limited to date. Given the increased computational power at lowered costs, simulation-based methods are becoming an even stronger alternative for conventional research methods. Moreover, these simulation models can have a user-interface component that allows policymakers to use them as a decision-support tool, especially in healthcare and pandemic control (Asgary, Najafabadi, et al., 2020; Asgary et al., 2021). In this paper, we contribute to filling this gap by suggesting a novel hybrid simulation-based methodology. This methodology utilizes a combination of agent-based and discrete event simulations, to generate contact mixing matrices based on existing and predefined workflows, individual schedules, and behaviors in special settings. While we have already applied this method in some healthcare settings, the method introduced here can be easily replicated in other settings where human interaction takes place. This method can be applied in schools, Page 4 hospitals, shopping malls, long-term care facilities, offices, manufacturing facilities, places of worship, sports facilities, museums, factories, etc., to generate contact matrices. Generating a contact matrix from simulations is accomplished in four steps: 1. Develop a 2D/3D model of the setting 2. Define agents’ workflow and behavior in the setting 3. Run the simulation and record fine-grained agent-to-agent data 4. Generate contacts mixing matrices The rest of this paper explains each of these steps in detail in three examples: 1. A simulation model of a hospital hemodialysis ward that utilizes this method. 2. A simulated Intensive Care Unit (ICU) that uses a Bayesian version of compound probabilities in calculation of the matrix. 3. A simulation of a large gathering that allows examining this method for a much larger number of agents. 2. Developing the Simulation Model 2.1.
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